|
|
Dynamical analysis, control, boundedness, and prediction for a fractional-order financial risk system |
Kehao Yang(杨轲皓)1, Song Zheng(郑松)1,†, Tianhu Yu(余天虎)2, Aceng Sambas3,4, Muhamad Deni Johansyah5, Hassan Saberi-Nik6, and Mohamad Afendee Mohamed3 |
1 School of Data Science, Zhejiang University of Finance & Economics, Hangzhou 3100181, China; 2 Department of Mathematics, Luoyang Normal University, Luoyang 471934, China; 3 Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Besut Campus, 22200, Terengganu, Malaysia; 4 Department of Mechanical Engineering, Universitas Muhammadiyah Tasikmalaya, Tasikmalaya, Jawa Barat, 46196, Indonesia; 5 Department of Mathematics, Universitas Padjajdaran, Jatinangor, Kabupaten Sumedang 45363, Indonesia; 6 Department of Mathematics and Statistics, University of Neyshabur, Neyshabur 9319774400, Iran |
|
|
Abstract This paper delves into the dynamical analysis, chaos control, Mittag-Leffler boundedness (MLB), and forecasting a fractional-order financial risk (FOFR) system through an absolute function term. To this end, the FOFR system is first proposed, and the adomian decomposition method (ADM) is employed to resolve this fractional-order system. The stability of equilibrium points and the corresponding control schemes are assessed, and several classical tools such as Lyapunov exponents (LE), bifurcation diagrams, complexity analysis (CA), and 0-1 test are further extended to analyze the dynamical behaviors of FOFR. Then the global Mittag-Leffler attractive set (MLAS) and Mittag-Leffler positive invariant set (MLPIS) for the proposed financial risk (FR) system are discussed. Finally, a proficient reservoir-computing (RC) method is applied to forecast the temporal evolution of the complex dynamics for the proposed system, and some simulations are carried out to show the effectiveness and feasibility of the present scheme.
|
Received: 06 July 2024
Revised: 06 September 2024
Accepted manuscript online: 14 September 2024
|
PACS:
|
05.45.-a
|
(Nonlinear dynamics and chaos)
|
|
05.45.Jn
|
(High-dimensional chaos)
|
|
05.45.Pq
|
(Numerical simulations of chaotic systems)
|
|
Fund: Project jointly supported by the National Natural Science Foundation of China (Grant No. 12372013), Program for Science and Technology Innovation Talents in Universities of Henan Province, China (Grant No. 24HASTIT034), the Natural Science Foundation of Henan Province, China (Grant No. 232300420122), the Humanities and Society Science Foundation from the Ministry of Education of China (Grant No. 19YJCZH265), China Postdoctoral Science Foundation (Grant No. 2019M651633), First Class Discipline of Zhejiang-A (Zhejiang University of Finance and EconomicsStatistics), the Collaborative Innovation Center for Data Science and Big Data Analysis (Zhejiang University of Finance and Economics-Statistics). |
Corresponding Authors:
Song Zheng
E-mail: szh070318@zufe.edu.cn
|
Cite this article:
Kehao Yang(杨轲皓), Song Zheng(郑松), Tianhu Yu(余天虎), Aceng Sambas, Muhamad Deni Johansyah, Hassan Saberi-Nik, and Mohamad Afendee Mohamed Dynamical analysis, control, boundedness, and prediction for a fractional-order financial risk system 2024 Chin. Phys. B 33 110501
|
[1] Podlubny I 1999 Fractional differential equations (New York: Academic Press) pp. 41-120 [2] Petráš I 2011 Fractional-Order Nonlinear Systems: Modeling, Analysis and Simulation (Berlin: Springer Science & Business Media) pp. 7-42 [3] Yuan L, Zheng S and Wei Z 2022 Eur. Phys. J. Spec. Top. 231 2477 [4] Chen L, Wu X, António M L, Li X, Li P and Wu R 2023 Commun. Nonlinear Sci. Numer. Simul. 125 107365 [5] Zhou X, Jiang D, Nkapkop J, Ahmad M, Fossi J, Tsafack N and Wu J 2024 Chin. Phys. B 33 040506 [6] Jin M X, Sun K H and He S B 2023 Chin. Phys. B 32 060501 [7] Zhang Y, Xiao M, Cao J and Zheng W 2022 IEEE Trans. Syst. Man Cyber.: Syst. 52 1731 [8] Xu C, Liu Z, Liao M and Yao L 2022 Expert Syst. Appl. 199 116859 [9] El-Mesady A, Elsonbaty A and Adel W 2022 Chaos, Solitons and Fractals 164 112716 [10] Partohaghighi M, Yusuf A and Bayram M 2022 Int. J. Appl. Comput. Math. 8 86 [11] Chen L, Yin H, Huang T, Yuan L, Zheng S and Yin L 2020 Neural Netw. 125 174 [12] Cui L, Luo W and Ou Q 2021 Chin. Phys. B 30 020501 [13] He S, Wang H and Sun K 2022 Chin. Phys. B 31 060501 [14] Vignesh D, He S and Banerjee S 2023 Appl. Math. Comput. 455 128111 [15] Tang Z, He S, Wang H, Sun K, Yao Z and Wu X 2024 Chin. Phys. B 33 030503 [16] Wang X, Xu B, Shi P and Li S 2022 IEEE Tran. Neural Net. Lear. Syst. 33 445 [17] Wu X, Fu L, He S, Yao Z, Wang H and Han J 2023 Resul. Phys. 52 106866 [18] Leonov G, Bunin A and Koksch N 1987 Z. Angew. Math. Mech. 67 649 [19] Leonov G and Kuznetsov N 2013 Int. J. Bifurc. Chaos 23 1330002 [20] Swinnerton-Dyer P 2001 Phys. Lett. A 281 161 [21] Liao X 2004 Sci. China Ser. E, Inform. Sci. 34 1404 [22] Jian J, Wu K and Wang B 2020 Physica A 540 123166 [23] Peng Q and Jian J 2021 Chaos, Solitons and Fractals 150 111072 [24] Ren L, Lin M, Abdulwahab A, Ma J and Saberi-Nik H 2023 Chaos, Solitons and Fractals 169 113275 [25] Zhang X, Liu X, Zheng Y and Liu C 2013 Chin. Phys. B 22 030509 [26] Tacha O, Volos C, Kyprianidis I, Stouboulos I, Vaidyanathan S and Pham V 2016 Appl. Math. Comput. 276 200 [27] Tacha O, Munoz-Pacheco J, Zambrano-Serrano E, Stouboulos I and Pham V 2018 Nonlinear Dyn. 94 1303 [28] Jahanshahi H, Sajjadi S, Bekiros S and Aly A 2021 Chaos, Solitons and Fractals 144 110698 [29] Festag S, Denzler J and Spreckelsen C 2022 J. Biomed. Inform. 129 104058 [30] Maleki M, Mahmoudi M, Wraith D and Pho K 2022 Travel Med. Infect. Dis. 37 101742 [31] Puri C, Kooijman G, Vanrumste B and Luca S 2022 IEEE J Biomed. Health Inform. 26 6126 [32] Chimmula V and Zhang L 2022 Chaos, Solitons and Fractals 135 109864 [33] Wang W, Shao J and Jumahong H 2023 Sci. Rep. 13 20359 [34] Maass W, Natschläger T and Markram H 2002 Neural Comput. 14 2531 [35] Jaeger H and Haas H 2004 Science 304 78 [36] Du C, Cai F, Zidan M, Ma W, Lee S and Lu W 2017 Nat. Commun. 8 1 [37] Song Q, Zhao X, Feng Z, An Y and Song B 2021 CCDC 3893-3897 [38] Na X, Ren W, Liu M, Han M 2023 IEEE Trans. Neural Netw. Lear. Syst. 34 9302 [39] Staniczenko P, Lee C and Jones N 2009 Phys. Rev. E 79 011915 [40] Shen E, Cai Z and Gu F 2005 Appl. Math. Mech. 26 1188 [41] Gottwald G and Melbourne I 2004 Proc. Roy. Soc. A-Math. Phys. Eng. Sci. 460 603 [42] Muhamad D, Aceng S, Zheng S, Khaled B, Sundarapandian V, Mohamed M and Mamat M 2023 Chaos, Solitons and Fractals 177 114283 [43] Cherruault Y and Adomian G 1993 Math. Comput. Model. 18 103 |
No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|